interactionRCS facilitates interpretation and presentation of results from a regression model (linear, logistic, Cox) where an interaction between the main predictor of interest X (binary or continuous) and another continuous covariate Z has been specified. In particular, interactionRCS allows for basic interaction assessment (i.e. log-linear/linear interaction models where a product term between the two predictors is included) as well as settings where the second covariate is flexibly modeled with restricted cubic splines. Confidence intervals for the predicted effect measures (beta, OR, HR) can be calculated with either bootstrap or the delta method. Lastly, interactionRCS produces a plot of the effect measure over levels of the other covariate.
To install the latest version of interactionRCS, type the following lines in a web-aware R environment.
if(!"devtools" %in% rownames(installed.packages())){
install.packages("devtools")
}
devtools::install_github("https://github.com/gmelloni/interactionRCS.git")
# or alternative devtools::install_git("https://github.com/gmelloni/interactionRCS.git")
library(interactionRCS)After estimating a regression model (linear, logistic, Cox) such as model<-glm(y~ ...) estimate and plot interactions with:
int<-estINT(model=model, ...)
plotINT(int, ...)For a detailed introduction to interactionRCS and code examples please refer to this vignette
Giorgio Melloni, Hong Xiong, Andrea Bellavia
TIMI study group, Department of Cardiovascular Medicine, Brigham and Womens Hospital / Harvard Medical School